Computational Scientist - Artificial Intelligence & Machine Learning - JR26290-3800

University of Chicago - Chicago, IL

Full-time

Chicago, IL

Educational Services

About the position

The University of Chicago Research Computing Center (RCC) is seeking a Computational Scientist specializing in Artificial Intelligence (AI) and Machine Learning (ML) to support a variety of research projects across multiple disciplines. This role involves developing software that facilitates data acquisition, ingestion, and integration for research initiatives. The successful candidate will assist in creating user interfaces and scalable back-end services aimed at automating and enhancing the scientific output of multi-institutional research projects. As a domain expert, the Computational Scientist will work closely with faculty, post-docs, and graduate students, providing guidance and support on projects that leverage machine learning and AI technologies. In this position, the Computational Scientist will be responsible for identifying, developing, and implementing computational methods and resources that advance research objectives. This includes independently proposing and executing practical solutions to research challenges, as well as developing and maintaining AI and machine learning pipelines for various applications, including images, video, speech, and unstructured text. The role requires strong problem-solving skills to address regression, classification, clustering, forecasting, and anomaly detection issues using established machine learning techniques. Additionally, the Computational Scientist will communicate complex technical information to diverse audiences, including faculty, students, and researchers. This includes creating and presenting tutorials, hands-on workshops, and documentation to train the research community. The role also involves contributing to grant proposals by articulating the relationship between research goals and data resources, as well as evaluating past and present technologies to inform the development of new tools. The successful candidate will ensure that all new tools undergo quality control reviews and provide timely systems support and updates, including conducting information security assessments and risk analyses of the computing environment.

Responsibilities

• Support applications of Artificial Intelligence (AI) in various research disciplines and serve as the domain expert.
• Work closely with faculty to identify, develop, and implement useful computational methods and resources that support or advance their research.
• Independently and proactively propose and execute practical solutions to research challenges.
• Develop and implement AI and machine-learning based methods for different use cases: images, video, speech, unstructured text, etc.
• Develop, maintain, and support data analysis, AI and Machine Learning pipelines.
• Confidently solve regression, classification, clustering, forecasting, and anomaly detection problems using established machine learning techniques.
• Communicate highly technical information to numerous audiences, including faculty, students, researchers, and staff.
• Teach others and learn new techniques.
• Help faculty with grant proposals by contributing sections describing the interplay between research objectives and new or expanded data resources.
• Create and present tutorials, hands-on workshops, and documentation to train the research community.
• Develops and presents technical training materials and web-based documentation.
• Ensures timely systems support and updates.
• Assists in conducting information security assessments and risk analysis of computing environment.
• Evaluates past and present technologies to help develop new tools.
• Ensures all the new tools have been through quality control reviews.
• Perform other related work as needed.

Requirements

• Minimum requirements include a college or university degree in a related field.
• Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.

Nice-to-haves

• Ph.D. in computer science, computer engineering, data science, or similar.
• Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience applying latest AI/ML techniques in computer vision and image classification analysis.
• Experience with one or more following AI/ML domains: Causal AI, Reinforcement Learning, Generative AI, NLP, Dimension Reduction, Computer Vision, Sequential Models.
• Experience using AI/ML techniques to solve real-world applications.
• Proficiency in Python.
• Experience with one or more Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn, MatplotLib, Seaborn, geopy, NLTK.
• Experience with one or more high-level programming languages such as C/C++, Matlab, or R.
• Experience with Git and in general with version control systems.